This invention relates to machine vision systems, and more particularly to an image registration method and apparatus for accurately registering an object to be inspected to reference data in order to detect flaws in the object.
Machine vision technology addresses the problem of detecting flaws in objects by analyzing flaws in images obtained from the objects. An image is deemed to be flawed when it does not accurately represent the object it was intended to represent. If the object is flawed, such as might result from wear and tear on the object, or an error in manufacturing the object, a flawed image results. In this case, the flawed image may accurately represent the flawed object, but nevertheless deviates from the image of what an unflawed object would be.
Most of the existing optical defect and flaw detection tools in the electronics manufacturing industry and many in other industries are based on an image-to-reference comparison scheme whereby a digitized product image is compared to some kind of reference data to detect abnormalities. Golden Template Comparison (GTC) is one such machine vision technique commonly employed to detect flaws and defects in images of 2-dimensional scenes that do not suffer from geometric distortion. According to this technique, an image of an object to be tested for flaws is compared to a xe2x80x9cgolden templatexe2x80x9d, or reference image. A golden template image is the mean of a plurality of good sample images. To perform the comparison, the input image and the golden template image must be registered with each other, and then subtracted. The resulting difference image is then analyzed for features that indicate flaws or defects in the test object.
In practice, it is found that the quality of the reference image and the accuracy of the registration step are major determinants of the performance of image-to-reference comparison systems. There exist conventional registration devices which register 3 dimensional reference images with images obtained from 3 dimensional test objects. These devices operate by obtaining and analyzing the input image to find identifying features of the imaged object. Identifying features of an object are high-curvature areas of the object such as corners, edges, and holes. The high curvature areas are then matched to the reference image, or vice versa, usually by techniques which calculate normals to the high curvature surfaces.
Alternatively, the object may be scanned to locate high-curvature areas and an input image of the high curvature areas created. The input image is then compared to the reference image to match structures on the reference image with identifying features of the object. Differences in location and orientation the reference image and corresponding identifying features of the input image are identified. Once these differences are found, a rotation matrix is used to update the orientation, or pose, of the reference image, the input image, or both. This process is repeated until there is less than an acceptable amount of misregistration. This approach is also typically implemented by determining normals to high curvature surface areas.
Determination of surface normals has a high degree of error for high curvature surfaces since methods of creating an input image of the object""s surface have a large degree of error at high curvature locations. Also, determination of the location and normal vectors for a large number of structures can become computationally burdensome.
Currently, there is a need for a system which efficiently and accurately registers an object""s input image to a corresponding reference image of the object.
A system for registering an input image to a reference image includes one or more imaging devices for obtaining an input image and a processor for comparing the image to the reference image. The processor includes a patch determining device for identifying low curvature portions of the reference image, and for matching the low curvature portions of the reference image to corresponding low curvature portions of the input image.
A transformation estimator provides a transformation matrix which maps points on the reference image 11 to corresponding points on the input image for registration.